#259: Extracting Retinal Vascular Networks Using Deep Learning Architecture

Y. M. Kassim and K. Palaniappan

IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017

deep learning, vessel segmentation, fundoscopy images, patches, convolution neural network

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Segmenting curvilinear structures in retinal images is important in early diagnosing of some diseases and monitoring their progress. In this work,we proposed an automatic segmentation method to extract vascular network in CHASE data set. We utilized deep learning framework to build our layers that accept image patches as input and produce the segmented image as output. Our work characterized by its robust detection for the vascular tree in spite of challenged conditions in these images such as illumination and contrast variations, tiny and close vessels, crossing area and optic disk thick boundary. We achieved the highest sensitivity among all the state of the art methods in the literature with comparable speci?city and better results in terms of accuracy. Our experimental results outperformed the best algorithm in sensitivity by 5%, as well as, we achieved an accuracy equal to 0.9630. In addition, we obtained better performance in terms of all evaluation methods after applying simple morphological operations to our ?nal segmentation results.